The Experts below are selected from a list of 267 Experts worldwide ranked by ideXlab platform
Eric Mjolsness - One of the best experts on this subject based on the ideXlab platform.
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a Convergence Proof for the softassign quadratic assignment algorithm
Neural Information Processing Systems, 1996Co-Authors: Anand Rangarajan, Alan L Yuille, Steven Gold, Eric MjolsnessAbstract:The softassign quadratic assignment algorithm has recently emerged as an effective strategy for a variety of optimization problems in pattern recognition and combinatorial optimization. While the effectiveness of the algorithm was demonstrated in thousands of simulations, there was no known Proof of Convergence. Here, we provide a Proof of Convergence for the most general form of the algorithm.
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NIPS - A Convergence Proof for the Softassign Quadratic Assignment Algorithm
1996Co-Authors: Anand Rangarajan, Alan L Yuille, Steven Gold, Eric MjolsnessAbstract:The softassign quadratic assignment algorithm has recently emerged as an effective strategy for a variety of optimization problems in pattern recognition and combinatorial optimization. While the effectiveness of the algorithm was demonstrated in thousands of simulations, there was no known Proof of Convergence. Here, we provide a Proof of Convergence for the most general form of the algorithm.
Richard M. Murray - One of the best experts on this subject based on the ideXlab platform.
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On quantized consensus by means of gossip algorithm - Part I: Convergence Proof
2009 American Control Conference, 2009Co-Authors: Javad Lavaei, Richard M. MurrayAbstract:This paper is concerned with the distributed averaging problem subject to a quantization constraint. Given a group of agents associated with scalar numbers, it is assumed that each pair of agents can communicate with a prescribed probability, and that the data being exchanged between them is quantized. In this part of the paper, it is proved that the stochastic gossip algorithm proposed in a recent paper leads to reaching the quantized consensus. Some important steady-state properties of the system (after reaching the consensus) are also derived. The results developed here hold true for any arbitrary quantization, provided that the tuning parameter of the gossip algorithm is chosen properly. The expected value of the Convergence time is lower and upper bounded in the second part of the paper.
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on quantized consensus by means of gossip algorithm part i Convergence Proof
American Control Conference, 2009Co-Authors: Javad Lavaei, Richard M. MurrayAbstract:This paper deals with the distributed averaging problem over a connected network of agents, subject to a quantization constraint. It is assumed that at each time update, only a pair of agents can update their own numbers in terms of the quantized data being exchanged. The agents are also required to communicate with one another in a stochastic fashion. In the first part of the paper, it was shown that the quantized consensus is reached by means of a stochastic gossip algorithm proposed in a recent paper, for any arbitrary quantization. The current part of the paper considers the expected value of the time at which the quantized consensus is reached. This quantity (corresponding to the worst case) is lower and upper bounded in terms of the topology of the graph, for uniform quantization. In particular, it is shown that the upper bound is related to the principal minors of the weighted Laplacian matrix. A convex optimization is also proposed to determine the set of probabilities (used to pick a pair of agents) which leads to the fast Convergence of the gossip algorithm.
Anand Rangarajan - One of the best experts on this subject based on the ideXlab platform.
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a Convergence Proof for the softassign quadratic assignment algorithm
Neural Information Processing Systems, 1996Co-Authors: Anand Rangarajan, Alan L Yuille, Steven Gold, Eric MjolsnessAbstract:The softassign quadratic assignment algorithm has recently emerged as an effective strategy for a variety of optimization problems in pattern recognition and combinatorial optimization. While the effectiveness of the algorithm was demonstrated in thousands of simulations, there was no known Proof of Convergence. Here, we provide a Proof of Convergence for the most general form of the algorithm.
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NIPS - A Convergence Proof for the Softassign Quadratic Assignment Algorithm
1996Co-Authors: Anand Rangarajan, Alan L Yuille, Steven Gold, Eric MjolsnessAbstract:The softassign quadratic assignment algorithm has recently emerged as an effective strategy for a variety of optimization problems in pattern recognition and combinatorial optimization. While the effectiveness of the algorithm was demonstrated in thousands of simulations, there was no known Proof of Convergence. Here, we provide a Proof of Convergence for the most general form of the algorithm.
Alan L Yuille - One of the best experts on this subject based on the ideXlab platform.
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a Convergence Proof for the softassign quadratic assignment algorithm
Neural Information Processing Systems, 1996Co-Authors: Anand Rangarajan, Alan L Yuille, Steven Gold, Eric MjolsnessAbstract:The softassign quadratic assignment algorithm has recently emerged as an effective strategy for a variety of optimization problems in pattern recognition and combinatorial optimization. While the effectiveness of the algorithm was demonstrated in thousands of simulations, there was no known Proof of Convergence. Here, we provide a Proof of Convergence for the most general form of the algorithm.
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NIPS - A Convergence Proof for the Softassign Quadratic Assignment Algorithm
1996Co-Authors: Anand Rangarajan, Alan L Yuille, Steven Gold, Eric MjolsnessAbstract:The softassign quadratic assignment algorithm has recently emerged as an effective strategy for a variety of optimization problems in pattern recognition and combinatorial optimization. While the effectiveness of the algorithm was demonstrated in thousands of simulations, there was no known Proof of Convergence. Here, we provide a Proof of Convergence for the most general form of the algorithm.
Steven Gold - One of the best experts on this subject based on the ideXlab platform.
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a Convergence Proof for the softassign quadratic assignment algorithm
Neural Information Processing Systems, 1996Co-Authors: Anand Rangarajan, Alan L Yuille, Steven Gold, Eric MjolsnessAbstract:The softassign quadratic assignment algorithm has recently emerged as an effective strategy for a variety of optimization problems in pattern recognition and combinatorial optimization. While the effectiveness of the algorithm was demonstrated in thousands of simulations, there was no known Proof of Convergence. Here, we provide a Proof of Convergence for the most general form of the algorithm.
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NIPS - A Convergence Proof for the Softassign Quadratic Assignment Algorithm
1996Co-Authors: Anand Rangarajan, Alan L Yuille, Steven Gold, Eric MjolsnessAbstract:The softassign quadratic assignment algorithm has recently emerged as an effective strategy for a variety of optimization problems in pattern recognition and combinatorial optimization. While the effectiveness of the algorithm was demonstrated in thousands of simulations, there was no known Proof of Convergence. Here, we provide a Proof of Convergence for the most general form of the algorithm.